Ebook Credit scoring with boosted decision trees
The accurate assessment of consumer credit risk is of uttermost importance for lending organizations. Credit scoring is a widely used technique that helps financial institutions evaluate the likelihood for a credit applicant to default on the financial obligation and decide whether to grant credit or not. The precise judgment of the creditworthiness of applicants allows financial institutions to increase the volume of granted credit while minimizing possible losses. The credit industry has experienced a tremendous growth in the past few decades (Crook et al., 2007). The increased number of potential applicants impelled the development of sophisticated techniques that automate the credit approval procedure and supervise the financial health of the borrower. The large volume of loan portfolios also imply that modest improvements in scoring accuracy may result in significant savings for financial institutions (West, 2000).
The goal of a credit scoring model is to classify credit applicants into two classes: the “good credit” class that is liable to reimburse the financial obligation and the “bad credit” class that should be denied credit due to the high probability of defaulting on the financial obligation. The classification is contingent on sociodemographic characteristics of the borrower (such as age, education level, occupation and income), the repayment performance on previous loans and the type of loan. These models are also applicable to small businesses since these may be regarded as extensions of an individual costumer. In the last few decades, various quantitative methods were proposed in the literature to evaluate consumer loans and improve the credit scoring accuracy (for a review, see e.g. Crook et al., 2007). These models can be grouped into parametric and non-parametric or data mining models. The most popular parametric models are the linear discriminant analysis and the logistic regression.
Linear discriminant analysis was the first parametric technique suggested for credit scoring purposes (Reichert et al., 1983). This approach has attracted criticism due to the categorical nature of the data and the fact that the covariance matrices of the good credit and bad credit groups are typically distinct. The logistic regression (Wiginton, 1980) allows to overcome these deficiencies and became a common credit scoring tool of practitioners in financial institutions. Non-parametric techniques applied to credit scoring include the k-nearest neighbor (Henley and Hand, 1996), decision trees (Frydman et al., 1985; Davis et al., 1992), artificial neural networks (Jensen, 1992), genetic programming (Ong et al., 2005) and support vector machines (Baesens et al., 2003). More recently, research on hybrid data mining approaches has shown promising results (Lee et al., 2002; Hsieh, 2005; Lee and Chen, 2002).
While the pursuit of better classifiers for credit scoring applications is a crucial research effort, improved accuracies can be easily achieved by aggregating scores predicted by an ensemble of individual classifiers. West et al. (2005) found that the accuracy of an ensemble of neural networks is superior to that of a single neural network in credit scoring and bankruptcy prediction applications. This paper proposes a credit scoring model of consumer loans based on boosted decision trees, a powerful learning technique in which an ensemble of decision trees is developed to form a classifier given by a weighted majority vote of classifications predicted by the individual trees.
The decision trees are grown sequentially using reweighted training sets. If an instance is misclassified by a tree its weight is increased. Consequently, the predominance of “hard-to-classify” instances in the training sample increases with the number of grown trees. The performance of boosted decision trees is evaluated using two real world credit datasets from the UC Irvine Machine Learning Repository (Asuncion and Newman, 2007) and compared to that of a multilayer perceptron and a support vector machine. The rest of this paper is organized as follows. In the next section, boosted decision trees are introduced. This is followed by a description of the data sets and a comparison of the predictive accuracy of the models. A discussion of the relative contribution of the attributes to separate the good credit and bad credit classes is also given. Section 4 concludes the paper.
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